Multi-model ensemble hydrologic prediction using Bayesian model averaging

By Qingyun Duan, Newsha K. Ajami, Xiaogang Gao, Soroosh Sorooshian. 2007

687 citations recorded by the source corpus.

Abstract Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box–Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/180d9974700e8336023a3d9a43fd0d3703508f31

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In Water Science and Technology

Citations in the corpus

Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, methodology): “The BMA method has also been applied to multimodel hydrology and climate problems [ Duan et al., 2007; Min and Hense, 2007].”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “For the selection of candidate models for BMA, it is suggested to use previous research and theory to specify the set of model structures that are plausible and supported by data (Gelman and Rubin, 1995; Raftery et al., 2005; Duan et al., 2007; Vrugt and Robinson, 2007; Montgomery and Nyhan, 2008).”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, methodology): “…Parent, 1998], Bayesian Recursive Estimation [Thiemann et al., 2001], Bayesian hierarchical models [Huard and Mailhot, 2008; Kavetski et al., 2006a; Kuczera et al., 2006], instrumental‐variable methods [Young, 1998], Bayesian model averaging [Duan et al., 2007; Marshall et al., 2007] and others.”
Ensemble learning: A survey (2018, methodology): “In the Bayesian combination method, weighting is based on the probabilistic likelihood of obtaining the model given the entire dataset (Duan, Ajami, Gao, & Sorooshian, 2007).”
Ensemble evaluation of hydrological model hypotheses (2010, methodology): “Formal Bayesian Model Averaging (BMA) was used in hydrological applications by Vrugt et al. [2006], Duan et al. [2007] , Ajami et al. [2007] and Vrugt and Robinson [2007].”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, background): “…Bayesian model averaging (BMA), introduced by Raftery et al. (2005), has enjoyed success in forecasting weather quantities such as 2-m temperature, sea level pressure, precipitation (Sloughter et al. 2007), and wind speed (Sloughter et al. 2010), as well as hydrologic streamflow (Duan et al. 2007).”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, background): “Citation: Gong, W., H. V. Gupta, D. Yang, K. Sricharan, and A. O. Hero III (2013), Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach, Water Resour.”
Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments (2010, positive): “BMA has been successfully applied in streamflow prediction (Duan et al., 2007), groundwater hydrology (Neuman, 2003), soil hydraulic (Ẅohling and Vrugt, 2008) and surface temperature, and sea level pressure (Vrugt et al.”
A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method (2017, methodology): “Among various ANNs, the Back-Propagation (BP) Neural Network is one of the most frequently used methods (Gao and Zhang 2014) and is adopted in this study to acquire the relationship between NDVI and precipitation/air temperature.”